Abstract

This project is focused on the validation and evaluation of a predictor designed
for a wireless routing protocol named Optimized Link State Routing or OLSR,
which is a proactive routing protocol, based on link-state algorithms and
specifically designed for Mobile Ad Hoc Networks or MANETs.
The aim of this predictor is to avoid the flooding of control messages when they
are duplicated, i.e. redundant, since they are sent periodically although
containing always the same information. Then, using this predictor, it is
possible to reduce, to a greater or lesser extent, the overall traffic of the
network, which can be traduced in an whole network improvement by reducing
the energy and CPU consumption, decreasing the network congestion, and
then, reducing loss rate.
First of all, in order to analyze the impact of the predictor, it has been done an
in-depth study the original OLSR protocol, specially focused on the generation
and processing of the topology control messages or TC, which are the
responsible of flooding the network topology. Then, tools as Bonnmotion 2.0
and Network Simulator 2 have been used to generate different wireless mobile
scenarios and simulate it, respectively. By means of the analysis of the
resulting trace files, using AWK software, it has been characterized the OLSR
behavior under the different proposed scenarios.
Once known the OLSR functioning, it has been explained, validated and
evaluated the impact of the introduction of the predictor (OLSRp). It has been
defined the more appropriate scenarios for which the introduction of the
predictor has a bigger positive impact, i.e. where more TC messages are
redundant and its transmission throughout the network can be avoided.
Finally, in order to know the environmental impact in terms of energy
consumption reduction, it has been analyzed the energy saving achieved
thanks to the predictor. Once again, the most appropriate scenarios in which
the predictor impact produces the higher saving of energy have been
characterized and exemplified in realistic scenarios where use it to take the
most advantage of the predictor.